The PostgreSQL Production Checklist

This series covered a lot of ground, from choosing a data type to sharding a cluster. This final article pulls it into a single checklist you can run against a real deployment, before launch and periodically after. None of it is new; it's the whole masterclass distilled into "did you do the thing." Use it as a review, and each item links back to the article that explains the why.

This is part 40 and the last article of the PostgreSQL Masterclass, following scaling strategies.

Schema and data modeling

The foundation, from the first module. Getting these right prevents whole categories of bugs:

• Data types are correct: numeric for money, timestamptz for time, text over varchar(n), bigint for growing keys, native uuid and jsonb where used.
• Constraints are in place: NOT NULL on required columns, UNIQUE where duplicates must be prevented, CHECK for invariants, foreign keys with a deliberate ON DELETE action.
• Foreign key columns are indexed (Postgres does not do this automatically).
• Known-shape data is in real columns; JSONB is used only for genuinely variable data.

Indexing and query performance

From the indexing module. This is where most performance lives:

• Every frequent query filters, joins, or sorts on indexed columns; no unexpected sequential scans on large tables.
• Indexed columns aren't hidden behind functions in WHERE clauses.
• pg_stat_statements is enabled, and you've reviewed the top queries by total time.
• Unused (zero-scan) indexes have been dropped; they only tax writes.
• Pagination uses keyset, not deep OFFSET, on large lists.

Transactions and concurrency

From the concurrency module. These prevent the subtle bugs that only appear under load:

• Transactions are short and don't wrap slow external calls.
• Read-modify-write logic is protected with SELECT FOR UPDATE or a suitable isolation level.
• Application code retries on serialization failures (40001) and deadlocks (40P01).
• Rows are locked in a consistent order to avoid deadlocks.
• Concurrent user edits use optimistic locking (a version column) where lost updates would matter.

Operations and maintenance

From the operations articles. The background health that decays silently if ignored:

• Autovacuum is tuned for busy tables (lower scale factor, adequate cost limit and workers), not left at defaults on a large database.
• No long-running or idle in transaction sessions are blocking vacuum; idle_in_transaction_session_timeout is set.
• Table and index bloat is monitored, with online rebuild tools (REINDEX CONCURRENTLY, pg_repack) available.
• Transaction ID age is monitored for wraparound risk.

Backups and recovery

From the backups article. The safety net you hope never to need and must have:

• Backups use physical base backups plus WAL archiving for point-in-time recovery, not just periodic dumps, through a real tool or managed service.
• Backups are stored offsite (separate region or provider) and encrypted.
• Restores are tested regularly, and the recovery procedure is documented with a known RTO.
• You understand that replicas are not backups.

High availability

From the HA article, if uptime requirements call for it:

• Automated failover is in place (managed service or Patroni), not manual promotion.
• Split-brain is prevented via quorum consensus and fencing.
• The application connects through a stable endpoint that follows the primary and retries through the failover window.
• RTO and RPO are defined, and the setup meets them.

Connections and scaling

From the scaling articles:

• A connection pooler (PgBouncer or managed) sits between the app and Postgres, in transaction mode, with a small pool.
• The application handles transaction-pooling caveats (no reliance on cross-transaction session state).
• Read-heavy, lag-tolerant queries are routed to replicas where replicas exist, with read-your-own-writes kept on the primary.
• Large, time-based tables are partitioned with automated partition creation and retention.

Monitoring and alerting

From the monitoring article, tying it together:

• Query performance, connections, replication lag, and table health are monitored over time.
• Alerts fire on leading indicators: replication lag, connections nearing the limit, disk usage with headroom, transaction ID age, long-running sessions, and a falling cache hit ratio.
• Disk space has real headroom and alerts before it's critical, since a full disk stops Postgres cold.

Security

Drawing on the RLS article and general practice:

• The application connects as a least-privilege role, not a superuser or the table owner.
• Multi-tenant isolation is enforced (row-level security, or rigorously tested application filtering).
• Connections use TLS, and credentials live in a secrets manager, not in code.
• Postgres is not exposed to the public internet; access is restricted to known networks.

Using this checklist

Run it before a launch, and revisit it quarterly, because a database that was healthy at launch drifts: tables grow past their indexes, new queries arrive without review, autovacuum falls behind, backups silently break. The checklist is a periodic audit, not a one-time gate.

That closes the PostgreSQL Masterclass. Across these 40 articles we went from picking a column type to running a replicated, backed-up, monitored cluster that scales. The throughline is that PostgreSQL rewards understanding: nearly every hard problem has a clean built-in answer once you know the mechanism behind it. Learn the mechanisms, run the checklist, and Postgres will handle far more than most teams ever ask of it.

The series continues from here into the rest of the backend and data track, with a Redis Masterclass next, applying the same depth to caching, data structures, and the patterns that pair Redis with the PostgreSQL foundation built here.